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Record W4413911839 · doi:10.3390/cells14171367

Attention-Deficit Hyperactivity Disorder (ADHD): A Comprehensive Overview of the Mechanistic Insights from Human Studies to Animal Models

2025· review· en· W4413911839 on OpenAlexafffund
M Yacoub, Sophia Smith, Badra Abbas, Fahad Iqbal, Cham Maher Othman Jazieh, Nada Saed Homod Al Shaer, Collin Luk, Naweed I. Syed

Bibliographic record

VenueCells · 2025
Typereview
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsHotchkiss Brain InstituteAlberta Children's HospitalUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAttention deficit hyperactivity disorderNeuroscienceAttention deficitPsychologyAnimal modelPsychiatryMedicine

Abstract

fetched live from OpenAlex

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by persistent inattention, hyperactivity, and impulsivity. Despite its considerable global prevalence, key gaps remain in our understanding of the structural and molecular changes underlying ADHD which complicate adult diagnosis, as symptoms present differently from those observed during childhood ADHD. On the other hand, while psychostimulants effectively mitigate some symptoms, significant controversy surrounds their long-term effects on cognition, learning, and memory, and day-to-day living. Moreover, our understanding of how various medications given to alleviate ADHD symptoms during pregnancy impact the developing fetal brain also remains largely unexplored. Here, we discuss the subtle differences between ADHD in children and adults and how these symptoms alter brain development and maturation. We further examine changes in monoamine signaling in ADHD and how psychostimulant and non-pharmacological treatments modulate these neural networks. We evaluate and discuss findings as they pertain to the long-term use of ADHD medications, including in utero exposure, on cognitive outcomes, and contextualize these findings with mechanistic insights from animal models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.181
GPT teacher head0.413
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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